Coordinated control of vehicle cohorts

ABSTRACT

Exemplary embodiments include unique apparatuses, methods and systems providing coordinated control of vehicle cohorts. Certain embodiments perform an optimization of a vehicle cohort model and a plurality of vehicle models to determine vehicle cohort operating parameters and individual vehicle operating parameters in order to minimize total vehicle cohort power or a total vehicle cohort energy over a route of travel. Such embodiments further determine vehicle operating commands executable by vehicles of the cohort to implement the vehicle cohort operating parameters and individual vehicle operating parameters. Such vehicle operating commands may be transmitted to, received by and, executed by vehicles of the vehicle cohort.

CROSS REFERENCE

This application claims the benefit of and priority to U.S. ApplicationNo. 62/579,478 filed Oct. 31, 2017 which is hereby incorporated byreference.

BACKGROUND

The present application relates to coordinated control of vehiclecohorts. A number of techniques have been proposed for coordinating theoperation of vehicle cohorts. Such proposals generally involve groupingvehicles into cohorts, sometimes referred to as platoons, andcoordinating acceleration and braking of vehicles in the cohort in orderto allow for a closer headway between vehicles by eliminating reactingdistance needed for human reaction. Existing proposals suffer from anumber of drawbacks and limitations. There remains a significant needfor the unique apparatuses, methods, systems and techniques ofcoordinated control of vehicle cohorts disclosed herein.

DISCLOSURE OF ILLUSTRATIVE EMBODIMENTS

For the purposes of clearly, concisely and exactly describingillustrative embodiments of the present disclosure, the manner andprocess of making and using the same, and to enable the practice, makingand use of the same, reference will now be made to certain exemplaryembodiments, including those illustrated in the figures, and specificlanguage will be used to describe the same. It shall nevertheless beunderstood that no limitation of the scope of the invention is therebycreated, and that the invention includes and protects such alterations,modifications, and further applications of the exemplary embodiments aswould occur to one skilled in the art.

SUMMARY OF THE DISCLOSURE

Exemplary embodiments include unique apparatuses, methods and systemsproviding coordinated control of vehicle cohorts. Certain embodimentsperform an optimization of a vehicle cohort model and a plurality ofvehicle models to determine vehicle cohort operating parameters andindividual vehicle operating parameters in order to minimize totalvehicle cohort power or a total vehicle cohort energy over a route oftravel. Such embodiments further determine vehicle operating commandsexecutable by vehicles of the cohort to implement the vehicle cohortoperating parameters and individual vehicle operating parameters. Suchvehicle operating commands may be transmitted to, received by and,executed by vehicles of the vehicle cohort. Further embodiments, forms,objects, features, advantages, aspects, and benefits shall becomeapparent from the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of certain aspects of an exemplaryvehicle cohort control system.

FIG. 2 is a schematic illustration of certain aspects of an exemplaryvehicle cohort controller.

FIG. 3 is a flow diagram of certain aspects of an exemplary vehiclecohort control process.

FIG. 4 is a schematic illustration of certain aspects of an exemplaryvehicle cohort.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

With reference to FIG. 1 there is illustrated a schematic view of anexemplary vehicle cohort control system 100 including a vehicle cohort103 comprising a plurality of vehicles 101 a, 101 b, 101 c andpotentially additional vehicles as denoted by ellipsis 101 n. Vehicles101 a, 101 b, 101 c . . . 101 n may be referred to individually as avehicle 101 and collectively as vehicles 101 or collectively as vehiclecohort 103. While vehicle cohort 103 is illustrated as comprising atleast three vehicles 101, it shall be appreciated vehicle cohortsaccording to the present disclosure may comprise any number of two ormore vehicles controlled or operating in a coordinated manner.

Vehicles 101 may be a variety of types of vehicles such as trucks,tractor-trailers, box trucks, busses, and passenger cars, among others.The vehicles 101 illustrated in FIG. 1 are depicted as tractor trailers,but any type of vehicle is thus contemplated herein. Some embodimentscontemplate that vehicles 101 may each be the same or similar types ofvehicles, for example, in the case of a commonly managed vehicle fleet.Some embodiments contemplate that vehicles 101 may comprise differenttypes or classes of vehicles, for example, semi tractor-trailers andpassenger cars. Each vehicle 101 includes a prime mover 102, such as aninternal combustion engine, an electric motor, or hybrid engine-electricmotor system, which is structured to output power to propel the vehicle101. Some embodiments contemplate that prime movers 102 may each be thesame or similar types of prime movers, for example, in the case of acommonly managed vehicle fleet. Some embodiments contemplate that primemovers 102 may comprise different types or classes of prime movers, forexample, prime movers of different sizes, powers or types (e.g., dieselengine powertrains, gasoline engine powertrains, natural gaspowertrains, hybrid-electric powertrains, and electric powertrains). Forease of description prime mover 102 may be referred to herein as anengine, however, it shall be understood that these references also applyto and include other types of prime movers.

Vehicle cohort 103 is illustrated in a cohort mode of operation,sometimes referred to as platooning or platooning operation, in whichthe vehicles act in a coordinated manner to reduce net fuel consumptionby the vehicle cohort 103 and increase net operating efficiency of thevehicle cohort 103 relative to uncoordinated operation. Each vehicle 101in vehicle cohort 103 utilizes one or more environmental sensors todetermine its positioning relative to other vehicles in vehicle cohort103. Examples of the types of sensor systems that may be utilizedinclude radar systems, lidar systems, proximity sensor systems, andcombinations of these and/or other sensor systems. Each vehicle 101 invehicle cohort 103 also includes a wireless communication systemallowing vehicle-to-vehicle (V2V) communication or vehicle-to-X (V2X)communication where X denotes a variety of possible types of externalnetworks.

Each vehicle 101 includes a vehicle electronic control system (VECS) 104which is structured to control and monitor operation of its respectivevehicle 101, as well as to participate in cohort mode coordinatedoperation as disclosed herein. Each VECS 104 may be configured toprovide autonomous or semi-autonomous control over the speed,positioning, prime mover operation and other internal system operationof its respective vehicle 101. Each VECS 104 typically comprises aplurality of integrated circuit-based electronic control units (ECU) orother control components which may be operatively coupled to one otherover a communication bus or network such as a controller area network(CAN) and which are structure to implement various controls, forexample, an engine ECU structure to control and monitor operation of anengine and engine accessories, a transmission ECU structured to controland monitor operation of a transmission, a wireless communication ECUstructured to control ex-vehicle wireless communications, and one ormore environmental sensor ECUs structured to control operation of anenvironmental sensor system may be provided. It shall be appreciatedthat the control logic and control processes disclosed herein may beperformed by controllers or controls which are implemented in dedicatedcontrol components of VECS 104 (e.g., in a dedicated ECU or otherdedicated control circuity) or may be implemented in a distributedfashion across multiple control components of VECS (e.g., throughcoordinated operation of an engine ECU, a transmission ECU, a wirelesscommunication ECU and an environmental sensor ECU).

The ECUs and other control components of VECs 104 may comprise ofdigital circuitry, analog circuitry, or a hybrid combination of both ofthese types. The ECUs and other control components of VECs 104 can beprogrammable, an integrated state machine, or a hybrid combinationthereof. The ECUs and other control components of VECs 104 can includeone or more Arithmetic Logic Units (ALUs), Central Processing Units(CPUs), memories, limiters, conditioners, filters, format converters, orthe like which are not shown to preserve clarity. In one form, thevehicle electronic control system 104 is of a programmable variety thatexecutes algorithms and processes data in accordance with operatinglogic that is defined by executable program instructions stored in anon-transitory memory medium (e.g., software or firmware). Alternativelyor additionally, operating logic for the vehicle electronic controlsystem 104 can be at least partially defined by hardwired logic or otherhardware.

The environmental sensor and wireless communication capabilities ofvehicles 101 allow their operation to be coordinated using direct orindirect communication. Such operation may be referred to as coordinatedoperation or cohort mode operation. For example, vehicles 101 mayaccelerate or brake simultaneously, or in a coordinated sequence,maintain a particular distance relative to one other, or maintain aparticular offset relative to one another. Coordinated operation alsoallows a closer following distance between vehicles by compensating foror eliminating reacting distance needed for human reaction. Coordinatedoperation of vehicle cohort 103 further allows for operation thatreduces net fuel consumption or increases net efficiency of the vehiclecohort 103. One or more of the vehicles 101 may in some embodiments, beequipped with aerodynamic capability (wind assist panels on cab &trailer, aerodynamic tractor body) that creates a laminar flow of air(tunnel effect) that greatly reduces air drag. Other vehicles amongvehicles 101 may be spaced close enough to the vehicle taking advantageof a wind break tunnel to increase fuel economy. It shall be appreciatedthat the controls disclosed herein can mitigate aerodynamic losses bothby adjusting vehicle following distance(s) and vehicle offset.

Coordinated operation of vehicle cohort 103 may be provided at least inpart by a vehicle cohort controller (VCC) 140. In the embodiment of FIG.1, VCC is provided as a cloud-based control system in operativecommunication with the VECS 104 of each of vehicles 101 via one or morecommunication networks 130. In other embodiments, VCC 140 may beprovided in a vehicle 101 and may be a part of a VECS 104 or implementedin an independent electronic control system. In other embodiments,certain functionalities of VCC 140 may be distributed among two or morevehicles or two or more VECS 104 or other vehicle-based electroniccontrol systems.

With reference to FIG. 2 there is illustrated a schematic depiction ofone exemplary form of VCC 140. In the illustrated embodiment, VCC 140 isstructured to perform an optimization of a total vehicle cohort power(P_(Total)) or a total vehicle cohort energy (E_(Total)) for operatingthe vehicle cohort over a given route of travel. It shall be appreciatedthat total energy and total power are related as described by equation(1): E_(Total)=∫₀ ^(T)P_(Total)dt, where E_(Total) is the total energyused by the cohort over the route of travel, P_(Total) is the totalpower used by the cohort over the route of travel, and T is the totaltime required for the vehicle cohort to complete the route of travel.Thus, a model or an optimization of total energy may be performed interms of either power or energy depending on computational convenienceand, as used herein a model or an optimization of total energy shall beunderstood to include a model or an optimization of total power.

VCC 140 is configured to and operable to model total vehicle cohortpower (e.g., the total power consumed by all vehicles in the vehiclecohort) as a function of the total power of each vehicle of the cohort.In one form VCC 140 may model total vehicle cohort power in accordancewith equation (2): P_(Total)=Σ_(i=1) ^(N)P_(T) _(i) where P_(Total) isthe instantaneous total vehicle cohort power, i is an integer denotingeach vehicle in the vehicle cohort, N is the total number of vehicles inthe cohort, and P_(T) _(i) is the instantaneous power consumed by thei-th vehicle (i.e., each individual vehicle).

VCC 140 may be further configured to and operable to model the totalvehicle cohort power as a function of a set of equations accounting fordifferent aspects of the total power consumed by each individual vehiclein accordance with equation (3):

$P_{Total} = {{\sum\limits_{i = 1}^{N}( {P_{T_{i}}^{roll} + P_{T_{i}}^{{Ptr}_{loss}} + P_{T_{i}}^{acl}} )} + {\sum\limits_{i = 1}^{N}{P_{T_{i}}^{Fan}( {{D\lbrack\;\rbrack},Y,O,{Aw},{Fb},{Vs}} )}} + {\sum\limits_{i = 1}^{N}{P_{T_{i}}^{aero}( {{D\lbrack\;\rbrack},Y,O,{Aw},C_{Db},{Fb},{Vs}} )}} + {\sum\limits_{i = 1}^{N}{P_{T_{i}}^{eng\_ acc}({Dc})}}}$

where, for each i-th vehicle (i.e., each individual vehicle), P_(T) _(i)^(roll) is the power a vehicle consumes to overcome rolling resistance,P_(T) _(i) ^(Ptr) ^(loss) is the power a vehicle consumes to overcomepowertrain losses, P_(T) _(i) ^(acl) is the power a vehicle consumes toachieve acceleration, P_(T) _(i) ^(Fan) is the power a vehicle consumesthrough parasite losses attributable to the radiator fan(s), P_(T) _(i)^(aero) is the power a vehicle consumes to overcome aerodynamic losses,P_(T) _(i) ^(eng) ^(_) ^(acc) is the power consumed by operation ofengine components such as fuel pump, water pump, etc. and otheraccessories such as air compressors, air conditioners and otheraccessories which may consume mechanical or electrical power generatedby an engine, D[ ] is set of distances between a vehicle and one or moreadjacent vehicles in the cohort which may be aligned, offset and/orangled in any direction relative to one another, Y is vehicle yaw, O isoffset in the direction transverse to the direction of travel (e.g.,lateral position in a lane) between a vehicle and the vehicle adjacentto it in the cohort, Aw is the angle of wind incident to a vehicle, Fbis the wind speed or base wind flow, C_(Db) is the baseline dragcoefficient, Vs is the vehicle road speed and Dc is accessory dutycycle.

It shall be appreciated that models in accordance with equation (2) andequation (3) may be utilized in modeling vehicle cohort operation over agiven route of travel or a given operating horizon by performing anintegration or summation over the time or distance over a given route oftravel or a given operating horizon.

With reference to FIG. 4 there is illustrated one non-limiting exampleof a vehicle cohort 400 including a vehicle 401 and a vehicle 402. Theembodiment of FIG. 4 provides one example of the parameters Aw, Fb, D[ ]and O relative to vehicles 401 and 402, as well as parameters Vs (withVs₁ corresponding to vehicle 401 and Vs₂ corresponding to vehicle 402)and Y (with Y₁ corresponding to vehicle 401 and Y₂ corresponding tovehicle 402). In the embodiment of FIG. 4, the distance between vehiclesD[ ] is defined as the distance relative to corresponding points alongthe length of vehicles, in this instance the respective front centerpoints of the vehicles. Under this definition a value of D[ ]=0 wouldindicate that the vehicles are at the same distance along the route oftravel as may occur when vehicles are offset in a side-by-sidearrangement. In other embodiments the distance between vehicles D[ ] maybe defined as the distance relative to different points along the lengthof vehicles, for example, the distance between rear center point of aforward vehicle and the front center point of a following vehicle. Inthe embodiment of FIG. 4, the angles Aw and Y are defined relative toforward road direction vector (e.g., direction of a lane line). In otherembodiments, these angles may be defined relative to other coordinatesystems, for example, using a constant vector such as a vector pointingto zero-degrees North.

VCC 140 is configured to and operable to maintain a plurality of vehiclemodels 142 in accordance with equation (3) or another computer model ofvehicle operation based upon input received by VCC 140. In general termsthe inputs received by VCC 140 may be categorized as internal vehicleinformation 202 which is specific to each vehicle in a cohort, externalstatic vehicle information 204 which may be the same for each vehicle inthe cohort or may vary among vehicles, and external dynamic vehicleinformation 206 which is specific to each vehicle in a cohort.

Internal vehicle information 202 comprises certain information fromwhich the values of the vehicle models 142 (e.g., the values of theterms of equation (3)) may be determined. In general, internal vehicleinformation 202 comprises information about vehicle components andimmediate surroundings that vary over time and is therefore onlyprecisely valid for a given vehicle at a given point in time, althoughthe degree of time precision may vary in practice. Internal vehicleinformation is typically determined using and available from on-boardsensors and communication with other vehicle powertrain components.Examples of internal vehicle information include but are not limited tovehicle mass, gear selection, engine speed, vehicle speed Vs, vehicletransmission state and based transmission loss, vehicle acceleration,vehicle distance D, vehicle yaw Y, vehicle offset O, vehicle wind angleAw, vehicle temperatures such as coolant temperature and oiltemperature, ambient temperature, ambient humidity, and current roadgrade.

External static vehicle information 204 generally comprises informationabout conditions outside of the vehicle that do not change with time orchange relatively slowly (e.g., daily, weekly, monthly etc.). Suchinformation may be determined from map based data, communication withother devices outside of the vehicle. Examples of such informationinclude but are not limited to road grade over a given route of travel,intersections, curvature, charging locations in the case of electricvehicles, and construction zones over a given route of travel to nameseveral examples. External static vehicle information 204 may be used todetermine look-ahead information or a prediction of the futureconditions that will be encountered by the vehicle cohort. Look-aheadinformation, in turn, may be taken into account when performing theoptimizations and determining operating commands for vehicles asdescribed herein.

It shall be appreciated that the calculations or computations disclosedherein may be performed at a given instant or offline over a horizon orrepeated as a form of model predictive control (MPC). Furthermore, itshould be appreciated that future, look-ahead information for allparameters utilized by such calculations or computations may beavailable as a look-ahead information and that the optimizationsdisclosed herein may be performed using the look-ahead information.

External dynamic vehicle information 206 generally comprises informationabout things outside of the vehicle that change frequently over time.Such information may be determined using V2X communication. Examples ofsuch information include but not limited to traffic density, weatherforecast, traffic light phases, road conditions, fuel price at a givenlocation and electricity price at a given location. External dynamicvehicle information 206 may be taken into account when performing theoptimizations and determining operating commands for vehicles asdescribed herein.

VCC 140 is configured to and operable to maintain a vehicle cohort model144 in accordance with the equation(s) or another computer model ofvehicle operation based upon the vehicle models 142. VCC 140 is furtherconfigured to and operable to operate an optimization engine 146 toperform an optimization on the terms of one or both of vehicle cohortmodel 144 and vehicle models 142. Optimization engine 146 may bestructured to perform the optimization using a number of techniquesincluding, for example, dynamic programming or dynamic optimizationtechniques, Pontryagin's maximum principle, convex optimizationtechniques, machine learning techniques, neural network or combinationsof these and other optimization techniques as would occur to one ofskill in the art with the benefit of the present disclosure. Regardlessof the particular techniques utilized, optimization engine 146 isstructured to perform an optimization, considers and determines bothvehicle cohort operating parameters and individual vehicle operatingparameters in order to minimize total vehicle cohort power (P_(Total))or a total vehicle cohort energy (E_(Total)).

Vehicle cohort operating parameters are parameters defining positioningof vehicles in the cohort relative to one another and may be expressedas the position of each vehicle relative to an absolute coordinatesystem or a relative coordinate system wherein the positioning of eachvehicle is defined relative to one or more other vehicles. The powerthat vehicles in the cohort consume to overcome aerodynamic losses(P_(T) _(i) ^(aero)) may be a primary factor in the optimization ofvehicle cohort operating parameters. In such instances, optimizationengine 146 may determine vehicle cohort operating parameters to defineabsolute or relative positions for each vehicle in the cohort in orderto minimize the term P_(T) _(i) ^(aero) for each vehicle as part of anoverall optimization. For example, in a model according to equation (3)the variables impacting the terms P_(T) _(i) ^(aero) are distancesbetween vehicles (D), vehicle yaw (Y) and offset (O), which arecontrollable by VCC 140, as well as wind angle (Aw) and wind speed (Fb),which are not controllable by VCC 140. Accordingly, optimization engine146 may be structured to determine values for the controllable variablesthat minimize the terms P_(T) _(i) ^(aero) while accounting for theeffect of the uncontrollable variables.

Individual vehicle operating parameters are parameters defining theinternal operating conditions of a given vehicle in the vehicle cohort,for example, gear selection, fueling, engine speed, radiator fanoperation. Each of the loads on the engine may be a primary factor inthe optimization of individual vehicle operating parameters.Accordingly, any engine operating command which influences vehicle loadmay be specified by and controlled in response to the optimization.

In certain embodiments optimization engine 146 is structured to performa sequential optimization in which the optimal vehicle cohort operatingparameters are first determined and the optimal individual vehicleoperating parameters which provide or are consistent with the optimalvehicle cohort operating parameters are then determined. Accordingly,optimization engine 146 may determine individual vehicle operatingparameters in order to minimize the terms P_(T) _(i) ^(roll), P_(T) _(i)^(Ptr) ^(loss) , P_(T) _(i) ^(acl), P_(T) _(i) ^(Fan), P_(T) _(i) ^(eng)^(_) ^(acc) while concurrently providing the optimal vehicle cohortoperating parameters specified by the minimization of the terms P_(T)_(i) ^(aero). In certain embodiments optimization engine 146 isstructured to perform a parallel optimization in which optimal vehiclecohort operating parameters and optimal individual vehicle operatingparameters are determined in parallel and differences between theresulting vehicle operating commands are then reconciled by a furtheroptimization that minimizes losses or power increases that are createdthrough the reconciliation. In certain embodiments, optimization engine146 is structured to perform an iterative optimization which repeatseither a sequential or a parallel optimization a plurality of times toconverge on a final set of vehicle operating commands.

It shall be appreciated that the optimization techniques described abovemay be implemented in connection with a look-ahead horizon so that theoptimizations consider future predicted model states and determineoptimal vehicle cohort operating parameters and optimal individualvehicle operating parameters that minimize total vehicle cohort power(P_(Total)) or total vehicle cohort energy (E_(Total)) over thelook-ahead horizon or the total mission.

VCC 140 further includes a command generator 148 which is configured toand operable to determine a plurality of operating commands 210 forrespective vehicles of the cohort that can be transmitted to andexecuted by the vehicles in order to provide and implement the optimizedvehicle cohort operating parameters and the optimized individual vehicleoperating parameters.

From the foregoing description it can be seen that VCC 140 may determineand cause to be executed a number of vehicle control commands. A numberof significant but non-limiting examples of which shall now bedescribed. In certain optimization scenarios, commands may be generatedto permit higher engine torque for a predetermined and typically limitedperiod of time of distance, for example, a torque limit increase may beprovided on an uphill to maintain the vehicle alignment and position ina cohort.

In certain optimization scenarios, heavier vehicles which may not haveenough torque to maintain following distance or alignment with respectto another on an uphill which would cause a cascading effect ofincreased aero drag and then increased power demand to climb the hill.In such scenarios, torque increase commands may be generated for theheavier vehicles in order to maintain the optimal following distances oralignments of the cohort. In such scenarios, lighter vehicles canalternatively or additionally slow down to appropriate speed to maintainthe optimal distance and alignment with respect to other vehicles.

Certain optimization scenarios, distance between vehicles in a cohortmay be decreased as the cohort travels on an uphill grade. Suchoptimization scenarios take advantage of the fact that on an uphillgrade, brake power required to stop the vehicle in given distance issmall due to mass and grade force. Thus, for a given braking power, thevehicle can be stopped in shorter distance. Accordingly, followingdistance may be decreases which has the benefit of reducing the powerrequired to overcome aerodynamic drag.

Certain optimization scenarios account for the fact that on an uphillgrade, vehicle engines can become warm quickly, requiring the radiatorfan to run, thereby further increasing demand on engine. Smallerfollowing distances can compound engine heating due to decreases ram-airrequiring increasing fan operation. In such scenarios, look-aheadinformation, wind direction information, wind velocity information andvehicle position in the cohort can be utilized to proactively coolbefore the uphill grade is encountered. Such pre-cool down operation maybe running the engine fan in advance of a hill. Alternatively, theengine temperature limit triggering fan operation may be increased toallow the engine to slightly overheat on the uphill grade so as to avoidrunning engine fan in anticipation of an opportunity for increasedram-air cooling on a following downhill.

Certain optimization scenarios account for the fact that, on a downhill,different vehicles may accelerate faster or slower due to massdifferences and aerodynamic drag differences due to relative alignmentin the cohort. This may result in different friction brake and enginebrake usage among different vehicles in the cohort. In such scenarios,increasing the relative distances on downhill may be commanded to adjustdrag on vehicles to avoid faster accelerations.

Certain optimization scenarios provide coordinating neutral coasting ona downhill road grade. Neutral costing can be managed intelligently,some vehicles being in neutral gear to accelerate faster and somevehicles remaining coasting for longer duration, or reengaging earlierto maintain a good location in the cohort. A pre-cooling or enginecooling on downhill can be achieved by turning the fan on duringdownhill to increase engine motoring losses as well as changeaerodynamic signature.

Certain optimization scenarios mitigate impact of turns on winddirection and disturbance caused by it, by aligning vehicles laterallyto maintain sufficient drag to the vehicle while consuming less fuel, inpractical application this will be limited by lane to truck width, tolane width ratio, and wind direction. In such scenarios a vehicle may bealigned in a lane based on wind direction to account for extra drag andreduce yaw movement.

Certain optimization scenarios may take advantage of the fact that on adownhill road grade, there may be multiple options to control thevehicle speed, by creating different types of energy losses includingfriction brakes, engine brakes, and active aerodynamic devices which canbe employed on the vehicle to create extra drag and maintaining asufficient separation distance. Such scenarios may optimize selection ofthese options to reduce total power for a deceleration operation.

Certain optimization scenarios may adjust predictive cruise control(PCC) acceleration rates on two nearby trucks on pre-uphill orpre-downhill. For example, the rate of PCC acceleration may be increasedon a downhill to take advantage of downhill acceleration in view of anupcoming uphill which will accommodate close following distance.

With reference to FIG. 3 there is illustrated a flow diagram of anexemplary vehicle cohort control process 300. Process 300 includesoperation 302 in which a vehicle cohort controller such as VCC 140receives vehicle cohort information comprising internal vehicleinformation for a plurality of vehicles, external static vehicleinformation for the plurality of vehicles, and external dynamic vehicleinformation for the plurality of vehicles. From operation 302, process300 proceeds to operation 304.

Operation 304 performs an optimization using a vehicle cohort controllersuch as VCC 140 to determine a plurality of vehicle cohort operatingparameters and a plurality of individual vehicle operating parameterseffective to minimize total vehicle cohort power (P Total) or totalvehicle cohort energy (E_(Total)) In performing such an optimization,operation 304 may utilize the techniques described in connection withFIG. 2 or elsewhere herein. From operation 304, process 300 proceeds tooperation 306.

Operation 306 determines a plurality of vehicle operating commands whichare executable by respective vehicles in a cohort to provide cohort andvehicle operating conditions specified by vehicle cohort operatingparameters and a plurality of individual vehicle operating parameters.From operation 306, process 300 proceeds to operation 308. Operation 308transmits the operating commands to the vehicles of the cohort. Fromoperation 308, process 300 proceeds to operation 310. At operation 310the vehicles of the cohort receives the operating commands. Fromoperation 310, process 300 proceeds to operation 312. At operation 312each vehicle in the cohort executes the operation commands applicable tothat vehicle. From operation 312, process 300 proceeds to operation 314.

At operation 314 each vehicle determines internal vehicle informationcomprising information about vehicle components and immediatesurroundings that vary over time, such as internal vehicle information202. From operation 314, process 300 proceeds to operation 316. Atoperation 316 each of the vehicles in the cohort transmits its internalvehicle information to the vehicle cohort controller. From operation316, process 300 proceeds to operation 302.

Concurrently with the above operations, operation 320 determinesexternal static vehicle information for the plurality of vehicles andexternal dynamic vehicle information for the plurality of vehicles.Operation 320 may be performed externally to the vehicles of the cohortor at least in part internally to one or more vehicles of the cohort.Operation 320 may be structured to repeat periodically, to repeat whenupdated or changed external vehicle information is available or torepeat based on a request received from the vehicle cohort controller.From operation 320, process 300 proceeds to 322 which transmits theexternal static vehicle information for the plurality of vehicles andthe external dynamic vehicle information for the plurality of vehiclesto the vehicle cohort controller.

While illustrative embodiments of the disclosure have been illustratedand described in detail in the drawings and foregoing description, thesame is to be considered as illustrative and not restrictive incharacter, it being understood that only certain exemplary embodimentshave been shown and described and that all changes and modificationsthat come within the spirit of the claimed inventions are desired to beprotected. It should be understood that while the use of words such aspreferable, preferably, preferred or more preferred utilized in thedescription above indicate that the feature so described may be moredesirable, it nonetheless may not be necessary and embodiments lackingthe same may be contemplated as within the scope of the invention, thescope being defined by the claims that follow. In reading the claims, itis intended that when words such as “a,” “an,” “at least one,” or “atleast one portion” are used there is no intention to limit the claim toonly one item unless specifically stated to the contrary in the claim.When the language “at least a portion” and/or “a portion” is used theitem can include a portion and/or the entire item unless specificallystated to the contrary.

1. A system comprising: a vehicle cohort controller in operativecommunication with a plurality of vehicles of a vehicle cohort, thevehicle cohort controller being configured to: store a model of totalpower or energy consumed by operation of the vehicle cohort in one ormore non-transitory memory media, perform an optimization of total poweror energy consumed by operation of the vehicle cohort using the model,the optimization providing a plurality of optimized vehicle cohortoperating parameters defining positioning of the plurality of vehiclesrelative to one another and a plurality of optimized individual vehicleoperating parameters defining one or more internal operating conditionsof individual vehicles of the vehicle cohort, determine a plurality ofvehicle operating commands corresponding to respective ones of theplurality of vehicles, the plurality of vehicle operating commandsconfigured to modify operation of the plurality of vehicles to producethe optimized vehicle cohort operating parameters and the optimizedindividual vehicle operating parameters; and transmit the plurality ofvehicle operating commands to the respective ones of the plurality ofvehicles effective to modify operation of the plurality of vehicles toproduce the optimized vehicle cohort operating parameters and theplurality of optimized individual vehicle operating parameters.
 2. Thesystem of claim 1 wherein the plurality of optimized vehicle cohortoperating parameters comprise, for each of the plurality of vehicles, adistance in a direction of travel from one or more other vehicles of thevehicle cohort and an offset in a direction transverse to the directionof travel from one or more other vehicles of the vehicle cohort.
 3. Thesystem of claim 2 wherein the distance and the offset for each of theplurality of vehicles comprise variables in a component of the modelmodeling power consumed by each of the plurality of vehicles to overcomeaerodynamic loss.
 4. The system of claim 3 wherein the component of themodel modeling power consumed by each of the plurality of vehicles toovercome aerodynamic loss further includes variables accounting forvehicle yaw, wind speed, wind direction, and vehicle speed.
 5. Thesystem of claim 2 wherein the distance and the offset for each of theplurality of vehicles comprise variables in a component of the modelmodeling power consumed by operation of a fan of each of the pluralityof vehicles.
 6. The system of claim 5 wherein the component of the modelmodeling power consumed by operation of a fan of each of the pluralityof vehicles further includes variables accounting for vehicle yaw, windspeed, wind direction, and vehicle speed.
 7. The system of claim 1wherein the optimized individual vehicle operating parameters include apowertrain torque and the plurality of vehicle operating commandscomprise at least one torque limit increase corresponding to anindividual vehicle of the vehicle cohort.
 8. A method comprising:operating a vehicle cohort controller in operative communication with aplurality of vehicles of a vehicle cohort to perform the operations of:storing a model of total power or energy consumed by operation of thevehicle cohort in one or more non-transitory memory media, performing anoptimization of total power or energy consumed by operation of thevehicle cohort using the model, the optimization providing a pluralityof optimized vehicle cohort operating parameters defining positioning ofthe plurality of vehicles relative to one another and a plurality ofoptimized individual vehicle operating parameters defining one or moreinternal operating conditions of individual vehicles of the vehiclecohort, determining a plurality of vehicle operating commandscorresponding to respective ones of the plurality of vehicles, theplurality of vehicle operating commands configured to modify operationof the plurality of vehicles to produce the optimized vehicle cohortoperating parameters and the optimized individual vehicle operatingparameters; and transmitting the plurality of vehicle operating commandsto the respective ones of the plurality of vehicles effective to modifyoperation of the plurality of vehicles to produce the optimized vehiclecohort operating parameters and the plurality of optimized individualvehicle operating parameters.
 9. The method of claim 8 wherein theplurality of optimized vehicle cohort operating parameters comprise, foreach of the plurality of vehicles, a distance in a direction of travelfrom one or more other vehicles of the vehicle cohort and an offset in adirection transverse to the direction of travel from one or more othervehicles of the vehicle cohort.
 10. The method of claim 9 wherein thedistance and the offset for each of the plurality of vehicles comprisevariables in a component of the model modeling power consumed by each ofthe plurality of vehicles to overcome aerodynamic loss.
 11. The methodof claim 10 wherein the component of the model modeling power consumedby each of the plurality of vehicles to overcome aerodynamic lossfurther includes variables accounting for vehicle yaw, wind speed, winddirection, and vehicle speed.
 12. The method of claim 9 wherein thedistance and the offset for each of the plurality of vehicles comprisevariables in a component of the model modeling power consumed byoperation of a fan of each of the plurality of vehicles.
 13. The methodof claim 12 wherein the component of the model modeling power consumedby operation of a fan of each of the plurality of vehicles furtherincludes variables accounting for vehicle yaw, wind speed, winddirection, and vehicle speed.
 14. The method of claim 8 wherein theoptimized individual vehicle operating parameters include a powertraintorque and the plurality of vehicle operating commands comprise at leastone torque limit increase corresponding to an individual vehicle of thevehicle cohort.
 15. A method comprising: receiving with a vehicle cohortcontroller vehicle cohort information comprising at least one ofinternal vehicle information for a plurality of vehicles, externalstatic vehicle information for the plurality of vehicles, and externaldynamic vehicle information for the plurality of vehicles; performing anoptimization with the vehicle cohort controller, the optimizationoptimizing at least one of total cohort power and total cohort energyover a horizon for operating the vehicle cohort, the optimization beingcomputed by the vehicle cohort controller as a function of a pluralityof vehicle quantities each corresponding to a respective vehicle of thecohort over the horizon, the plurality of vehicle quantities beingcomputed as a function of vehicle cohort information corresponding tothe respective vehicle, the optimization providing optimized vehiclecohort operating parameters and optimized individual vehicle operatingparameters; determining with the vehicle cohort controller a pluralityof operating commands structured to control operation of respectivevehicles of the cohort to provide the optimized vehicle cohort operatingparameters and optimized individual vehicle operating parameters;receiving with the plurality of vehicles in the cohort the plurality ofoperating commands; and controlling operation of one or more respectivevehicles of the cohort in accordance with one or more of the pluralityof operating commands corresponding to the one or more respectivevehicles.
 16. The method of claim 15 wherein the optimization utilizes avehicle cohort model including a plurality of vehicle models.
 17. Themethod of claim 15 wherein the optimization is configured to optimizeone of total vehicle cohort energy and total vehicle cohort power over aroute of travel.
 18. The method of claim 15 wherein the optimizationutilizes look-ahead information to predict future operating andenvironmental conditions for the cohort and for the plurality ofvehicles and performs a net optimization over a horizon of thelook-ahead information.
 19. The method of claim 15 wherein the act ofoptimizing comprises a sequential optimization in which optimal vehiclecohort operating parameters are first determined and optimal vehicleoperating parameters which provide or are consistent with the optimalvehicle cohort operating parameters are second determined.
 20. Themethod of claim 15 wherein the act of optimizing comprises a paralleloptimization in which optimal vehicle cohort operating parameters andoptimal vehicle operating parameters are determined in parallel.